Automated Classification of Medical-Billing Data

Abstract : When building a data pipeline to process medical claims there are many instances where automated classification schemes could be used to improve speed and efficiency. Medical bills can be classified by the statutory environment which determines appropriate adjudication of payment disputes. We refer to this classification result as the adjudication type of a bill. This classification can be used to determine appropriate payment for medical services.Using a set of 182,811 medical bills, we develop a procedure to quickly and accurately determine the correct adjudication type. A simple naïve Bayes classifier based on training set class occurrences gives 92.8% accuracy, which can be remarkably improved by instead presenting these probabilities to an artificial neural network, yielding 96.8 ±0.5 % accuracy.
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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.389-399, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_46〉
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R. Crandall, K. Lynagh, T. Mehoke, N. Pepper. Automated Classification of Medical-Billing Data. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.389-399, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_46〉. 〈hal-01571454〉

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